{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from icepyx import icesat2data as ipd\n",
    "import numpy as np\n",
    "import os\n",
    "import h5py\n",
    "import shutil\n",
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "earthdata_emails = {'tsnow03':'tasha.snow@colorado.edu',\n",
    "                    'fperez': 'fernando.perez@berkeley.edu',\n",
    "                    'alicecima':'alice_cima@berkeley.edu',\n",
    "                    'fsapienza': 'fsapienza@berkeley.edu'\n",
    "                      # add your name here\n",
    "                   }\n",
    "\n",
    "user = 'fsapienza'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ATL03 Retrieval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Auxiliary functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def delta_lat(lat, lon, delta_m):\n",
    "    return 180 * delta_m / ( np.pi * 6371000 )\n",
    "\n",
    "def delta_lon(lat, lon, delta_m):\n",
    "    return 180 * delta_m / ( np.pi * 6371000 * np.cos(lat * np.pi / 180) )\n",
    "\n",
    "def filter(string, substr): \n",
    "    return [str for str in string if\n",
    "             any(sub in str for sub in substr)] \n",
    "\n",
    "def file_in_dir(path):\n",
    "    return [f for f in listdir(path) if isfile(join(path, f))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieval function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_atl03 (lat, lon, date_range, delta_m, path = \"new_ATL03\"):\n",
    "    \n",
    "    \"\"\"\n",
    "    Read a ATL03 file based and retieve individual photons in a window around a\n",
    "    desired latitide, longitud and a range of dates. \n",
    "    \"\"\"\n",
    "    \n",
    "    # Spatial extend\n",
    "    \n",
    "    window_lat = delta_lat(lat, my_lon, delta_m)\n",
    "    window_lon = delta_lon(lat, my_lon, delta_m)\n",
    "    \n",
    "    spatial_extent = [ my_lon - window_lon, my_lat - window_lat, my_lon + window_lon, my_lat + window_lat ]\n",
    "    spatial_extent = [ float(x) for x in spatial_extent ]\n",
    "    \n",
    "    # Retreiving the data \n",
    "    \n",
    "    region_a = ipd.Icesat2Data('ATL03', spatial_extent, date_range)\n",
    "    region_a.avail_granules(ids=True)\n",
    "    region_a.earthdata_login(user, earthdata_emails[user])\n",
    "    region_a.order_vars.append(var_list=['lat_ph', \"lon_ph\", \"h_ph\"])\n",
    "    region_a.subsetparams(Coverage=region_a.order_vars.wanted)\n",
    "    region_a.order_granules()\n",
    "    \n",
    "    region_a.download_granules(path)\n",
    "    \n",
    "    # Accessing one individual file\n",
    "    \n",
    "    flist = file_in_dir(path)\n",
    "    assert len(flist) == 1, \"There is not an unique file in the folder\"\n",
    "    fname = path + \"/\" + flist[0]\n",
    "    \n",
    "    with h5py.File(fname, 'r') as fi:\n",
    "        \n",
    "        print(fi.keys())\n",
    "        \n",
    "        my_gt = filter(fi.keys(), [\"gt\"])[0]          # TODO == I should not put a zero here\n",
    "        \n",
    "        lat_ph = fi[my_gt]['heights'][\"lat_ph\"][:]\n",
    "        lon_ph = fi[my_gt]['heights'][\"lon_ph\"][:]\n",
    "        h_ph   = fi[my_gt]['heights'][\"h_ph\"][:]\n",
    "  \n",
    "    df = pd.DataFrame.from_dict({\"h_ph\": h_ph,\n",
    "                                 \"lon_ph\": lon_ph,\n",
    "                                 \"lat_ph\": lat_ph} )\n",
    "    \n",
    "    df = df [ (df[\"lat_ph\"] < lat + window_lat) & (df[\"lon_ph\"] < lon + window_lon) &\n",
    "              (df[\"lat_ph\"] > lat - window_lat) & (df[\"lon_ph\"] > lon - window_lon) ]\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 1: Finding a lake in Greenland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_lat = 68.4547\n",
    "my_lon = -47.6758\n",
    "date_range = ['2019-08-01','2019-08-03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Earthdata Login password:  ···········\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of data order requests is  1  for  1  granules.\n",
      "Data request  1  of  1  is submitting to NSIDC\n",
      "order ID:  5000000701958\n",
      "Initial status of your order request at NSIDC is:  processing\n",
      "Your order status is still  processing  at NSIDC. Please continue waiting... this may take a few moments.\n",
      "Your order is: complete\n",
      "Beginning download of zipped output...\n",
      "Data request 5000000701958 of  1  order(s) is downloaded.\n",
      "Download complete\n",
      "<KeysViewHDF5 ['METADATA', 'ancillary_data', 'gt2l', 'gt2r', 'orbit_info']>\n"
     ]
    }
   ],
   "source": [
    "pho_the_ph = read_atl03(my_lat, my_lon, date_range, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f405409c910>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2 = pho_the_ph.plot.scatter(x='lat_ph',\n",
    "                       y='h_ph',\n",
    "                       #c='lat_ph',\n",
    "                       s = 0.02) # colormap='viridis'\n",
    "plt.axvline(x = my_lat)\n",
    "plt.axvline(x = my_lat + delta_lat(my_lat, my_lon, 20))\n",
    "plt.axvline(x = my_lat - delta_lat(my_lat, my_lon, 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A histogram can help us to see that there are two different surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  5.,  11.,   5.,  14.,  13.,  19.,  22.,  16.,  11.,  13.,  22.,\n",
       "         16.,  24.,  19.,  18.,  18.,  21.,  15.,   8.,  21.,  14.,  26.,\n",
       "         22.,  13.,  19.,  16.,  14.,  23.,  19.,  24.,  27.,  28.,  18.,\n",
       "         33.,  29.,  33.,  33.,  31.,  35.,  38.,  29.,  38.,  37.,  34.,\n",
       "         25.,  29.,  34.,  51.,  84., 224., 482., 481., 239.,  33.,  14.,\n",
       "         19.,  20., 720.,  12.,  17.,  17.,  15.,  16.,  11.,  12.,  18.,\n",
       "         10.,  23.,  16.,  17.,  15.,  16.,  14.,  17.,  12.,  15.,  16.,\n",
       "         19.,  15.,  15.,  16.,  19.,   5.,  17.,   9.,  23.,  13.,  18.,\n",
       "         10.,  18.,  10.,  20.,  11.,   8.,  20.,   9.,  15.,  13.,   5.,\n",
       "          4.]),\n",
       " array([1589.4906, 1590.3362, 1591.1818, 1592.0273, 1592.8729, 1593.7185,\n",
       "        1594.5641, 1595.4097, 1596.2552, 1597.1008, 1597.9464, 1598.792 ,\n",
       "        1599.6376, 1600.4832, 1601.3287, 1602.1743, 1603.0199, 1603.8654,\n",
       "        1604.7109, 1605.5565, 1606.4021, 1607.2477, 1608.0933, 1608.9388,\n",
       "        1609.7844, 1610.63  , 1611.4756, 1612.3212, 1613.1667, 1614.0123,\n",
       "        1614.8579, 1615.7035, 1616.5491, 1617.3947, 1618.2402, 1619.0858,\n",
       "        1619.9314, 1620.777 , 1621.6226, 1622.4681, 1623.3137, 1624.1593,\n",
       "        1625.0049, 1625.8505, 1626.696 , 1627.5416, 1628.3872, 1629.2328,\n",
       "        1630.0784, 1630.924 , 1631.7695, 1632.615 , 1633.4606, 1634.3062,\n",
       "        1635.1517, 1635.9973, 1636.8429, 1637.6885, 1638.534 , 1639.3796,\n",
       "        1640.2252, 1641.0708, 1641.9164, 1642.762 , 1643.6075, 1644.4531,\n",
       "        1645.2987, 1646.1443, 1646.9899, 1647.8354, 1648.681 , 1649.5266,\n",
       "        1650.3722, 1651.2178, 1652.0634, 1652.9089, 1653.7545, 1654.6001,\n",
       "        1655.4457, 1656.2913, 1657.1368, 1657.9824, 1658.828 , 1659.6736,\n",
       "        1660.519 , 1661.3646, 1662.2102, 1663.0558, 1663.9014, 1664.747 ,\n",
       "        1665.5925, 1666.4381, 1667.2837, 1668.1293, 1668.9749, 1669.8204,\n",
       "        1670.666 , 1671.5116, 1672.3572, 1673.2028, 1674.0483],\n",
       "       dtype=float32),\n",
       " <a list of 100 Patch objects>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pho_the_ph[\"h_ph\"][8000:12000], 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automatic retrieval of many locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polygon(locations, wd = 100):\n",
    "\n",
    "    points = sorted(locations, key=lambda x: x[0])\n",
    "\n",
    "    polygon = []\n",
    "\n",
    "    for x in points:\n",
    "        # pairs of the form lon, lat\n",
    "\n",
    "        polygon.append( [ x[1] - delta_lon(x[0], x[1], wd), x[0] + delta_lat(x[0], x[1], wd)] )\n",
    "        polygon.append( [ x[1] + delta_lon(x[0], x[1], wd), x[0] + delta_lat(x[0], x[1], wd)] )\n",
    "\n",
    "    for x in reversed(points):\n",
    "\n",
    "        polygon.append( [ x[1] + delta_lon(x[0], x[1], wd), x[0] - delta_lat(x[0], x[1], wd)] )\n",
    "        polygon.append( [ x[1] - delta_lon(x[0], x[1], wd), x[0] - delta_lat(x[0], x[1], wd)] )\n",
    "\n",
    "    polygon.append(polygon[0])\n",
    "\n",
    "    return [ [float(x[0]), float(x[1])] for x in polygon]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
